Spatial Ecology and Macroecology
Practical - Week 1
(Department of Spatial Sciences)
2023-10-02
1. Data types
Opportunistic incidence records
- presence-only data from museum, herbarium collection or citizen-science initiatives
- single species, spatio-temporally specific, unknown absences
| PROS | CONS |
|---|---|
| huge amounts of data available, easily aggregated | often without details of effort/method, wide variation in data quality |
1. Data types
Presence-absence data
- data from inventories, checklists, atlas, acoustic sensors, DNA sampling or camera-trap sruveys
- multiple species, spatio-temporally specific, report searches that did not find the species (absences)
| PROS | CONS |
|---|---|
| absences are informative, area and effort are measured | less abundant (too time consuming), methods are species-specific |
1. Data types
Repeated surveys
- monitoring schemes, repeated atlas projects
- multiple species, over time, spatially defined, use a standardized protocol
| PRO | CONS |
|---|---|
| standardised protocols, multiple points in time | expensive: geographically restricted, usually temporally too |
1. Data types
Range-maps
- outlines of species distributions, IUCN ranges, field guides
- single species, expert-drawn
| PROS | CONS |
|---|---|
| rough estimates of the outer boundaries of areas within which species are likely to occur | large spatial and temporal uncertainties |
1. Data types
Structured
- clear survey design (location, target) and standardised sampling protocol
- site selection: preselected locations, sometimes stratified random
- metadata: informs about the survey methods
1. Data types
Semi-structured
- no survey design but little standardised sampling protocol
- site selection: free
- metadata: informs about the observation process and survey methods
1. Data types
Unstructured (opportunistic)
- no survey design and no standardised sampling protocol
- site selection: free
- metadata: almost non
1. Data types
Disaggregated
- precision is high, but completeness and representativeness are low.
1. Data types
Aggregated
- precision is low, but completeness and representativeness are high.
gbif.org
GBIF is an international network and data infrastructure funded by the world’s governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth.
obis.org
OBIS is a global open-access data and information clearing-house on marine biodiversity for science, conservation and sustainable development.
ebird.org
eBird’s goal is to gather birdwatcher’s knowledge and experience in the form of checklists of birds, archive it, and freely share it to power new data-driven approaches to science, conservation and education.
ebird.org
eBird’s goal is to gather birdwatcher’s knowledge and experience in the form of checklists of birds, archive it, and freely share it to power new data-driven approaches to science, conservation and education.
inaturalist.org
iNaturalist is one of the world’s most popular nature apps. It allows participants to contribute observations of any organism, or traces thereof, along with associated spatio-temporal metadata.
mol.org
Map of Life endeavors to provide ‘best-possible’ species range information and species lists for any geographic area. The Map of Life assembles and integrates different sources of data describing species distributions worldwide.
iucnredlist.org
IUCN’s (International Union for Conservation of Nature) Red List of Threatened Species has evolved to become the world’s most comprehensive information source on the global extinction risk status of animal, fungus and plant species.
rredlist: https://github.com/ropensci/rredlist
iucnredlist.org
IUCN’s (International Union for Conservation of Nature) Red List of Threatened Species has evolved to become the world’s most comprehensive information source on the global extinction risk status of animal, fungus and plant species.
bien.nceas.ucsb.edu/bien/
BIEN is a network of ecologists, botanists, and computer scientists working together to document global patterns of plant diversity, function and distribution.
sibbr.gov.br
SiBBr (Brazilian Biodiversity Information System) is an online platform that integrates data and information about biodiversity and ecosystems from different sources, making them accessible for different uses.
sibbr: https://github.com/sibbr
bto.org/our-science/projects/breeding-bird-survey
BBS (Breeding Bird Survey) involves thousands of volunteer birdwatchers carrying out standardised annual bird counts on randomly-located 1-km sites. It’s part of the NBN Atlas.
biotime.st-andrews.ac.uk
BioTime is an open access database global database of assemblage time series for quantifying and understanding biodiversity change.
BioTime Hub: https://github.com/bioTIMEHub
nhm.ac.uk/our-science/our-work/biodiversity/predicts
PREDICTS uses data on local biodiversity around the world to model how human activities affect biological communities. This biodiversity change is shown as the Biodiversity Intactness Index (BII).
3. Open Data
Open means anyone can freely access, use, modify, and share for any purpose.
3. Open Data: Data standards
Darwin Core is the internationally agreed data standard to facilitate the sharing of information about biological diversity.
dwc.tdwg.org
countryCode: The standard code for the country in which the Location occurs. Recommended best practice is to use an ISO 3166-1-alpha-2 country code.
recordedBy: A list (concatenated and separated) of names of people, groups, or organizations responsible for recording the original Occurrence.
3. Open Data: Licensing
Open data are licensed under open licenses. Some examples:
CC0: Public domain
CC-BY: Attribution
CC-BY-NC: Attribution - Non Commercial
CC-BY-SA: Attribution - Share Alike
3. Open Data: Data sharing
Data that are standardized and have an open licence can be shared :)
4. Data download through R
5. Data quality
Data are not ‘good’ or ‘bad’, the quality will depend on our goal.
Some things we can check:
- Base of the record (type of occurrence)
- Species names (taxonomic harmonisation)
- Spatial and temporal (accuracy / precision)
CoordinateCleaner: https://github.com/ropensci/CoordinateCleaner
Automated flagging of common spatial and temporal errors in data.
How are the records distributed?
We’ll get to this next week :)
How are the records distributed?
And finally, a simple trick to produce separate maps per order.
Any doubts?
